Various techniques are known of converting an image of a wide dynamic range (a range of a pixel value) such as an infrared image, and displaying the image on a display device of a narrow dynamic range.
For example, assume that an infrared image is captured by an infrared camera which can capture an image in a dynamic range of “the fourteenth power of 0 to 2”. When this infrared image is displayed on a display device which can display an image in the dynamic range of “0 to 255”, it is not possible to display this infrared image with a current pixel value. In this case, some conversion needs to be performed to display this infrared image on the display device. Thus, converting an image having a wide range of a pixel value into an image having a narrow range of a pixel value is referred to as “dynamic range compression”.
In Non Patent Literature 1, linear scaling (linear conversion) of converting a gray level of an original image is described. Linear scaling is a method of linearly mapping a pixel value of an input image (an infrared image in the above example) in a range (0 to 255 in the above example) which the pixel value can be displayed by, for example, a display device. FIG. 8 is an explanatory view illustrating a conversion function for linearly mapping (that is, performing linear scaling) a pixel value of an infrared image. With a graph illustrated in FIG. 8, the horizontal axis indicates a pixel value on an infrared image, and the vertical axis indicates a pixel value on a converted image. FIG. 8 illustrates that the pixel value of the infrared image in the range of 0 to 10000 is converted into a pixel value in the range of 0 to 255. FIG. 9 illustrates an image converted by linear scaling. By changing the gray level area of an input image in this way, it is possible to display an image having a wide range of a pixel value, on a display device having a narrow range of a pixel value like the image illustrated in FIG. 9.
Further, Non Patent Literature 1 also discloses Histogram Equalization (hereinafter “HE”). HE refers to a technique of performing conversion (histogram conversion) in order that a distribution (hereinafter “histogram”) of a frequency (the number of pixels) of each gray level in a given image becomes to be flat. Hereinafter, HE will be described using FIGS. 10 and 11. FIG. 10 is an explanatory view illustrating a brightness histogram of an image before conversion. Further, FIG. 11 is an explanatory view illustrating a (equalized) brightness histogram after conversion. In addition, in the following description, a pixel value is also referred to as a “brightness value”. With graphs illustrated in FIGS. 10 and 11, the horizontal axis indicates a brightness value on an infrared image, and the vertical axis indicates an appearance frequency of a brightness value. With the image represented by the graph illustrated in FIG. 10, a frequency in the range of a low brightness value is high, and a frequency in the range of a high brightness value is low. Hence, an image is converted by applying HE to this image to equalize the distribution of the brightness value as illustrated in FIG. 11. FIG. 12 is an image converted by applying HE. With HE, each gray level is evenly used, and therefore the change of the shading of an image after processing becomes more obvious than an image before processing. Further, a method of applying HE in units of local blocks is referred to Adaptive Histogram Equalization (hereinafter “AHE”). By applying AHE, it is possible to adjust a contrast according to the gray level of each block with respect to, for example, an image having a fine shading per local block.
In Non Patent Literature 2, MEAM (the method according to Aare Mallo) is described. With MEAM, an input image is separated into a low-frequency image and a high-frequency image, linear scaling is applied to the low-frequency image, gain amplifying processing is applied to a high-frequency image and both images are finally superimposed. Hereinafter, MEAM will be described using FIGS. 13 to 19.
FIG. 13 is a flowchart illustrating processing in MEAN. Further, FIGS. 14 to 19 are explanatory views illustrating images converted by each processing. Hereinafter, a case will be described where MEAM is applied to an image of a wide dynamic range (for example, the fourteenth power of 0 to 2) illustrated in FIG. 14. First, a lowpass filter is applied to an image f(x,y) illustrated in FIG. 14 (step S91) to extract a low-frequency image f(x,y)lp (FIG. 15). Further, linear scaling is applied to the extracted low-frequency image f(x,y)lp to extract an image g(x,y)lp (FIG. 16) (step S92). On the other hand, a high-frequency image f(x,y)hp (FIG. 17) is extracted from the image f(x,y) illustrated in FIG. 14 (step S93). Further, gain amplification processing is applied to the extracted high-frequency image f(x,y)hp to extract an image g(x,y)hp (FIG. 18) (step S94). Finally, the extracted images g(x,y)lp and g(x,y)hp are added (step S95). The added image is an image g(x,y)1 illustrated in FIG. 19. In addition, the image g(x,y)1 is adjusted according to a range of a specified image (step S96). Thus, by using MEAM, it is possible to improve the contrast of images of the low-frequency image and the high-frequency image, and, consequently, compress the dynamic range while preserving edge information (information showing a location at which the brightness changes more rapidly than the surrounding) included more in the high-frequency image.